Peng Hu

9.1k total citations
249 papers, 6.5k citations indexed

About

Peng Hu is a scholar working on Radiology, Nuclear Medicine and Imaging, Cardiology and Cardiovascular Medicine and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Peng Hu has authored 249 papers receiving a total of 6.5k indexed citations (citations by other indexed papers that have themselves been cited), including 186 papers in Radiology, Nuclear Medicine and Imaging, 64 papers in Cardiology and Cardiovascular Medicine and 40 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Peng Hu's work include Advanced MRI Techniques and Applications (167 papers), Cardiac Imaging and Diagnostics (65 papers) and MRI in cancer diagnosis (48 papers). Peng Hu is often cited by papers focused on Advanced MRI Techniques and Applications (167 papers), Cardiac Imaging and Diagnostics (65 papers) and MRI in cancer diagnosis (48 papers). Peng Hu collaborates with scholars based in United States, China and Germany. Peng Hu's co-authors include Krishna S. Nayak, Vicente Gilsanz, Kim‐Lien Nguyen, Michael I. Goran, J. Paul Finn, Fei Han, Ziwu Zhou, Jeffrey H Miller, Stanislas Rapacchi and Daniel L. Smith and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and American Journal of Clinical Nutrition.

In The Last Decade

Peng Hu

243 papers receiving 6.4k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Peng Hu United States 43 3.3k 1.6k 1.6k 1.2k 520 249 6.5k
Charles A. McKenzie Canada 40 3.6k 1.1× 778 0.5× 557 0.3× 2.0k 1.6× 816 1.6× 118 6.5k
Ronald Borra Netherlands 40 1.5k 0.5× 780 0.5× 990 0.6× 1.1k 0.9× 671 1.3× 124 4.8k
Håkan Åhlström Sweden 54 3.3k 1.0× 1.7k 1.0× 1.8k 1.1× 1.8k 1.5× 1.9k 3.7× 335 9.9k
Steffen Ringgaard Denmark 38 1.2k 0.3× 1.2k 0.8× 705 0.4× 654 0.5× 878 1.7× 186 4.6k
Ann Shimakawa United States 40 4.8k 1.4× 1.5k 0.9× 421 0.3× 1.4k 1.2× 797 1.5× 84 6.8k
M. Eline Kooi Netherlands 39 1.5k 0.5× 1.7k 1.0× 1.3k 0.8× 1.3k 1.0× 538 1.0× 162 5.3k
Jean H. Brittain United States 34 4.3k 1.3× 957 0.6× 450 0.3× 1.9k 1.5× 737 1.4× 78 6.5k
Joseph A. Fisher Canada 46 3.1k 0.9× 1.2k 0.8× 419 0.3× 1.2k 1.0× 461 0.9× 225 7.1k
Gavin Hamilton United States 42 2.5k 0.8× 983 0.6× 955 0.6× 5.4k 4.3× 783 1.5× 106 8.1k
Dimitrios C. Karampinos Germany 39 2.0k 0.6× 525 0.3× 1.0k 0.6× 571 0.5× 1.2k 2.3× 235 5.4k

Countries citing papers authored by Peng Hu

Since Specialization
Citations

This map shows the geographic impact of Peng Hu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Peng Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peng Hu more than expected).

Fields of papers citing papers by Peng Hu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peng Hu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Peng Hu. The network helps show where Peng Hu may publish in the future.

Co-authorship network of co-authors of Peng Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Peng Hu. A scholar is included among the top collaborators of Peng Hu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Peng Hu. Peng Hu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Arduino, Alessandro, Peng Hu, Kévin Moulin, et al.. (2025). Feasibility study of subject‐specific, brain specific‐absorption‐rate maps retrieved from MRI data. Magnetic Resonance in Medicine. 94(3). 1136–1151. 1 indexed citations
2.
3.
Zhang, Rui, et al.. (2025). Epicardial and paracardial adipose tissue quantification in short-axis cardiac cine MRI using deep learning. Magnetic Resonance Materials in Physics Biology and Medicine. 39(1). 97–108.
4.
Hua, Sha, Yiwen Gong, Chunying Liu, et al.. (2024). Free-breathing non-contrast T1ρ dispersion magnetic resonance imaging of myocardial interstitial fibrosis in comparison with extracellular volume fraction. Journal of Cardiovascular Magnetic Resonance. 26(2). 101093–101093. 1 indexed citations
5.
6.
Owolabi, Eyitayo Omolara, et al.. (2024). Association between total, regional and organ fat and type 2 diabetes risk factors among Latino youth: A longitudinal study. Pediatric Obesity. 20(1). e13185–e13185. 2 indexed citations
7.
Campbell‐Washburn, Adrienne, Kathryn E. Keenan, Peng Hu, et al.. (2023). Low‐field MRI: A report on the 2022 ISMRM workshop. Magnetic Resonance in Medicine. 90(4). 1682–1694. 23 indexed citations
8.
Soltero, Erica G., et al.. (2022). Organ fat in Latino youth at risk for type 2 diabetes. Pediatric Diabetes. 23(3). 286–290. 4 indexed citations
9.
Keenan, Kathryn E., Zydrunas Gimbutas, Andrew Dienstfrey, et al.. (2021). Multi-site, multi-platform comparison of MRI T1 measurement using the system phantom. PLoS ONE. 16(6). e0252966–e0252966. 28 indexed citations
10.
Bydder, Mark, Hui Han, Ashley E. Prosper, et al.. (2021). Slice encoding for the reduction of outflow signal artifacts in cine balanced SSFP imaging. Magnetic Resonance in Medicine. 86(4). 2034–2048. 1 indexed citations
11.
Rivenson, Yair, Ashley E. Prosper, Kevin de Haan, et al.. (2021). Automatic segmentation of peripheral arteries and veins in ferumoxytol‐enhanced MR angiography. Magnetic Resonance in Medicine. 87(2). 984–998. 5 indexed citations
12.
Bydder, Mark, Arash Bedayat, Ashley E. Prosper, et al.. (2021). Temporally aware volumetric generative adversarial network‐based MR image reconstruction with simultaneous respiratory motion compensation: Initial feasibility in 3D dynamic cine cardiac MRI. Magnetic Resonance in Medicine. 86(5). 2666–2683. 9 indexed citations
13.
Bydder, Mark, et al.. (2020). Retrospective respiratory motion correction in cardiac cine MRI reconstruction using adversarial autoencoder and unsupervised learning. NMR in Biomedicine. 34(2). e4433–e4433. 22 indexed citations
14.
15.
Gao, Yu, Anusha Kalbasi, William Hsu, et al.. (2020). Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs. Physics in Medicine and Biology. 65(17). 175006–175006. 43 indexed citations
16.
Krishnamurthy, Ramkumar, et al.. (2019). Recent Advances in Pediatric Brain, Spine, and Neuromuscular Magnetic Resonance Imaging Techniques. Pediatric Neurology. 96. 7–23. 8 indexed citations
17.
Chien, Aichi, et al.. (2018). Accelerated phase contrast MRI using hybrid one‐ and two‐sided flow encodings only (HOTFEO). NMR in Biomedicine. 31(5). e3904–e3904. 4 indexed citations
18.
Chalfant, James S, Michelle L. Smith, Peng Hu, et al.. (2012). Inverse association between brown adipose tissue activation and white adipose tissue accumulation in successfully treated pediatric malignancy. American Journal of Clinical Nutrition. 95(5). 1144–1149. 23 indexed citations
19.
Hu, Peng. (2012). Magnetic resonance techniques for fat quantification in obesity. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference. 1–10. 3 indexed citations
20.
Akçakaya, Mehmet, Peng Hu, Michael L. Chuang, et al.. (2011). Accelerated noncontrast‐enhanced pulmonary vein MRA with distributed compressed sensing. Journal of Magnetic Resonance Imaging. 33(5). 1248–1255. 26 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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